{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 задача\n",
    "\n",
    "1. Скачать данные по ссылке https://gbcdn.mrgcdn.ru/uploads/asset/5957199/attachment/bfc1915d98cae5a9ba0b846162e60285.csv\n",
    "2. Считать данные с помощью pandas\n",
    "3. Вывести на экран первые 5 строк\n",
    "4. Посмотреть на описание признаков и на их содержание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('https://gbcdn.mrgcdn.ru/uploads/asset/4248710/attachment/5f6b595e63350aa5b55f0ea72561b871.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>1339.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Macbook Air</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>898.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>250 G6</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>575.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>2537.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>MacBook Pro</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>1803.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company      Product   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Macbook Air  Ultrabook    13.3                            1440x900   \n",
       "2      HP       250 G6   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  MacBook Pro  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  MacBook Pro  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight  Price_euros  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg      1339.69  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg       898.94  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg       575.00  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg      2537.45  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg      1803.60  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('laptops.csv', sep=';')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 задача\n",
    "\n",
    "Проведите первичный анализ данных\n",
    "\n",
    "1. Изучите типы данных\n",
    "2. Найдите количество пропущенных ячеек в данных\n",
    "3. Посчитайте основные статистики по всем признакам и поизучайте их"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   Product           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   object \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   object \n",
      " 11  Price_euros       1303 non-null   float64\n",
      "dtypes: float64(2), object(10)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Inches</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.017191</td>\n",
       "      <td>1123.686992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.426304</td>\n",
       "      <td>699.009043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.100000</td>\n",
       "      <td>174.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.600000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.600000</td>\n",
       "      <td>1487.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18.400000</td>\n",
       "      <td>6099.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Inches  Price_euros\n",
       "count  1303.000000  1303.000000\n",
       "mean     15.017191  1123.686992\n",
       "std       1.426304   699.009043\n",
       "min      10.100000   174.000000\n",
       "25%      14.000000   599.000000\n",
       "50%      15.600000   977.000000\n",
       "75%      15.600000  1487.880000\n",
       "max      18.400000  6099.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "      <td>1303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>19</td>\n",
       "      <td>618</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>118</td>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "      <td>110</td>\n",
       "      <td>9</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Dell</td>\n",
       "      <td>XPS 13</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2kg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>297</td>\n",
       "      <td>30</td>\n",
       "      <td>727</td>\n",
       "      <td>507</td>\n",
       "      <td>190</td>\n",
       "      <td>619</td>\n",
       "      <td>412</td>\n",
       "      <td>281</td>\n",
       "      <td>1072</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Company Product  TypeName   ScreenResolution  \\\n",
       "count     1303    1303      1303               1303   \n",
       "unique      19     618         6                 40   \n",
       "top       Dell  XPS 13  Notebook  Full HD 1920x1080   \n",
       "freq       297      30       727                507   \n",
       "\n",
       "                               Cpu   Ram     Memory                    Gpu  \\\n",
       "count                         1303  1303       1303                   1303   \n",
       "unique                         118     9         39                    110   \n",
       "top     Intel Core i5 7200U 2.5GHz   8GB  256GB SSD  Intel HD Graphics 620   \n",
       "freq                           190   619        412                    281   \n",
       "\n",
       "             OpSys Weight  \n",
       "count         1303   1303  \n",
       "unique           9    179  \n",
       "top     Windows 10  2.2kg  \n",
       "freq          1072    121  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 задача\n",
    "\n",
    "Ответьте на несколько вопросов\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Ноутбуков от какой компании больше всего в наборе данных?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company\n",
       "Dell         297\n",
       "Lenovo       297\n",
       "HP           274\n",
       "Asus         158\n",
       "Acer         103\n",
       "MSI           54\n",
       "Toshiba       48\n",
       "Apple         21\n",
       "Samsung        9\n",
       "Razer          7\n",
       "Mediacom       7\n",
       "Microsoft      6\n",
       "Xiaomi         4\n",
       "Vero           4\n",
       "Chuwi          3\n",
       "Google         3\n",
       "Fujitsu        3\n",
       "LG             3\n",
       "Huawei         2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Какая минимальная и максимальная стоимость у ноутбуков в данных?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(174.0, 6099.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Price_euros'].min(), df['Price_euros'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Какой самый дорогой ноутбук в данных?\n",
    "Выведите все характеристики только по этому ноутбуку\n",
    "\n",
    "*Если таких ноутбуков несколько, то выводите их всех*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Razer</td>\n",
       "      <td>Blade Pro</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>4K Ultra HD / Touchscreen 3840x2160</td>\n",
       "      <td>Intel Core i7 7820HK 2.9GHz</td>\n",
       "      <td>32GB</td>\n",
       "      <td>1TB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1080</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.49kg</td>\n",
       "      <td>6099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>Razer</td>\n",
       "      <td>Blade Pro</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>4K Ultra HD / Touchscreen 3840x2160</td>\n",
       "      <td>Intel Core i7 7820HK 2.9GHz</td>\n",
       "      <td>32GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1080</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.49kg</td>\n",
       "      <td>5499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Thinkpad P51</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel 4K Ultra HD 3840x2160</td>\n",
       "      <td>Intel Xeon E3-1535M v6 3.1GHz</td>\n",
       "      <td>32GB</td>\n",
       "      <td>1TB SSD</td>\n",
       "      <td>Nvidia Quadro M2200M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.5kg</td>\n",
       "      <td>4899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>HP</td>\n",
       "      <td>Zbook 17</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Xeon E3-1535M v5 2.9GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia Quadro M2000M</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>3kg</td>\n",
       "      <td>4389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>Asus</td>\n",
       "      <td>ROG G701VO</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6820HK 2.7GHz</td>\n",
       "      <td>64GB</td>\n",
       "      <td>1TB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 980</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.58kg</td>\n",
       "      <td>3975.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company       Product     TypeName  Inches  \\\n",
       "196    Razer     Blade Pro       Gaming    17.3   \n",
       "830    Razer     Blade Pro       Gaming    17.3   \n",
       "610   Lenovo  Thinkpad P51     Notebook    15.6   \n",
       "749       HP      Zbook 17  Workstation    17.3   \n",
       "1066    Asus    ROG G701VO       Gaming    17.3   \n",
       "\n",
       "                         ScreenResolution                            Cpu  \\\n",
       "196   4K Ultra HD / Touchscreen 3840x2160    Intel Core i7 7820HK 2.9GHz   \n",
       "830   4K Ultra HD / Touchscreen 3840x2160    Intel Core i7 7820HK 2.9GHz   \n",
       "610       IPS Panel 4K Ultra HD 3840x2160  Intel Xeon E3-1535M v6 3.1GHz   \n",
       "749           IPS Panel Full HD 1920x1080  Intel Xeon E3-1535M v5 2.9GHz   \n",
       "1066          IPS Panel Full HD 1920x1080    Intel Core i7 6820HK 2.7GHz   \n",
       "\n",
       "       Ram     Memory                      Gpu       OpSys  Weight  \\\n",
       "196   32GB    1TB SSD  Nvidia GeForce GTX 1080  Windows 10  3.49kg   \n",
       "830   32GB  512GB SSD  Nvidia GeForce GTX 1080  Windows 10  3.49kg   \n",
       "610   32GB    1TB SSD     Nvidia Quadro M2200M  Windows 10   2.5kg   \n",
       "749   16GB  256GB SSD     Nvidia Quadro M2000M   Windows 7     3kg   \n",
       "1066  64GB    1TB SSD  Nvidia GeForce GTX 980   Windows 10  3.58kg   \n",
       "\n",
       "      Price_euros  \n",
       "196        6099.0  \n",
       "830        5499.0  \n",
       "610        4899.0  \n",
       "749        4389.0  \n",
       "1066       3975.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Price_euros', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company                                           Razer\n",
       "Product                                       Blade Pro\n",
       "TypeName                                         Gaming\n",
       "Inches                                             17.3\n",
       "ScreenResolution    4K Ultra HD / Touchscreen 3840x2160\n",
       "Cpu                         Intel Core i7 7820HK 2.9GHz\n",
       "Ram                                                32GB\n",
       "Memory                                          1TB SSD\n",
       "Gpu                             Nvidia GeForce GTX 1080\n",
       "OpSys                                        Windows 10\n",
       "Weight                                           3.49kg\n",
       "Price_euros                                      6099.0\n",
       "Name: 196, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Price_euros', ascending=False).iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 задача\n",
    "\n",
    "Ответьте на несколько вопросов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Найдите ноутбуки с самой маленькой диагональю в данных?\n",
    "\n",
    "Выведите только следующие характеристики:\n",
    "1. Компания\n",
    "2. Типа ноутбука\n",
    "3. Диагональ\n",
    "4. Стоимость\n",
    "\n",
    "*Если таких ноутбуков несколько, то выводите их всех*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>10.1</td>\n",
       "      <td>319.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>10.1</td>\n",
       "      <td>646.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>10.1</td>\n",
       "      <td>549.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>10.1</td>\n",
       "      <td>479.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Inches  Price_euros\n",
       "50    Lenovo  2 in 1 Convertible    10.1       319.00\n",
       "1082  Lenovo  2 in 1 Convertible    10.1       646.27\n",
       "1114  Lenovo  2 in 1 Convertible    10.1       549.00\n",
       "1126  Lenovo  2 in 1 Convertible    10.1       479.00"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Inches'] == df['Inches'].min()][['Company', 'TypeName', 'Inches', 'Price_euros']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Сколько стоит самый дорогой ноутбук у компании HP?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>HP</td>\n",
       "      <td>Zbook 17</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Xeon E3-1535M v5 2.9GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia Quadro M2000M</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>3kg</td>\n",
       "      <td>4389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>HP</td>\n",
       "      <td>ZBook 17</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6700HQ 2.6GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia Quadro M3000M</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>3kg</td>\n",
       "      <td>3949.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>HP</td>\n",
       "      <td>Elitebook Folio</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>12.5</td>\n",
       "      <td>4K Ultra HD / Touchscreen 3840x2160</td>\n",
       "      <td>Intel Core M 6Y75 1.2GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>240GB SSD</td>\n",
       "      <td>Intel HD Graphics 515</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.09kg</td>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>HP</td>\n",
       "      <td>ZBook 17</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6700HQ 2.6GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD FirePro W6150M</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>3.0kg</td>\n",
       "      <td>2899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>HP</td>\n",
       "      <td>EliteBook x360</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 7600U 2.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.28kg</td>\n",
       "      <td>2559.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company          Product            TypeName  Inches  \\\n",
       "749       HP         Zbook 17         Workstation    17.3   \n",
       "1136      HP         ZBook 17         Workstation    17.3   \n",
       "911       HP  Elitebook Folio           Ultrabook    12.5   \n",
       "1103      HP         ZBook 17         Workstation    17.3   \n",
       "857       HP   EliteBook x360  2 in 1 Convertible    13.3   \n",
       "\n",
       "                         ScreenResolution                            Cpu  \\\n",
       "749           IPS Panel Full HD 1920x1080  Intel Xeon E3-1535M v5 2.9GHz   \n",
       "1136          IPS Panel Full HD 1920x1080    Intel Core i7 6700HQ 2.6GHz   \n",
       "911   4K Ultra HD / Touchscreen 3840x2160       Intel Core M 6Y75 1.2GHz   \n",
       "1103          IPS Panel Full HD 1920x1080    Intel Core i7 6700HQ 2.6GHz   \n",
       "857       Full HD / Touchscreen 1920x1080     Intel Core i7 7600U 2.8GHz   \n",
       "\n",
       "       Ram     Memory                    Gpu       OpSys  Weight  Price_euros  \n",
       "749   16GB  256GB SSD   Nvidia Quadro M2000M   Windows 7     3kg       4389.0  \n",
       "1136   8GB  256GB SSD   Nvidia Quadro M3000M   Windows 7     3kg       3949.4  \n",
       "911    8GB  240GB SSD  Intel HD Graphics 515  Windows 10  1.09kg       3100.0  \n",
       "1103   8GB    1TB HDD     AMD FirePro W6150M   Windows 7   3.0kg       2899.0  \n",
       "857    8GB  256GB SSD  Intel HD Graphics 620  Windows 10  1.28kg       2559.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Company'] == 'HP'].sort_values('Price_euros', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4389.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Company'] == 'HP']['Price_euros'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Как много ноутбуков Ultrabook с 8GB RAM?\n",
    "\n",
    "Найдите сколько таких ультрабуков с 8GB ОЗУ в процентном соотношении относительно всех ультрабуков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.42857142857143"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ultra_8gb = df[(df['TypeName'] == 'Ultrabook') & (df['Ram'] == '8GB')].shape[0]\n",
    "ultra = df[df['TypeName'] == 'Ultrabook'].shape[0]\n",
    "\n",
    "ultra_8gb / ultra * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 задача\n",
    "\n",
    "Ответьте на несколько вопросов\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Выберите ноутбук клиенту\n",
    "\n",
    "Клиент хочет подобрать ноутбук с 8GB или 16GB ОЗУ на Windows 10 в стоимости до 500 евро, сколько у него вариантов?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    (df['OpSys'] == 'Windows 10') & \n",
    "    (df['Ram'].isin(['8GB', '16GB'])) &\n",
    "    (df['Price_euros'] < 500)\n",
    "].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Выберите ноутбук клиенту\n",
    "\n",
    "Клиент хочет подобрать ноутбук от MSI, с видеокартой Nvidia GeForce GTX 1050 Ti и главное не с диагональю 15.6. В какой ценовой категории вышли подобные ноутбуки?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Product</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>MSI</td>\n",
       "      <td>GL72M 7REX</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.7kg</td>\n",
       "      <td>1199.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Leopard GP72M</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.7kg</td>\n",
       "      <td>1349.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>MSI</td>\n",
       "      <td>GL72M 7REX</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.7kg</td>\n",
       "      <td>1348.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>MSI</td>\n",
       "      <td>GE72VR 6RF</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.7kg</td>\n",
       "      <td>1599.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156</th>\n",
       "      <td>MSI</td>\n",
       "      <td>GP72M 7REX</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.7kg</td>\n",
       "      <td>1492.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company        Product TypeName  Inches   ScreenResolution  \\\n",
       "330      MSI     GL72M 7REX   Gaming    17.3  Full HD 1920x1080   \n",
       "393      MSI  Leopard GP72M   Gaming    17.3  Full HD 1920x1080   \n",
       "724      MSI     GL72M 7REX   Gaming    17.3  Full HD 1920x1080   \n",
       "917      MSI     GE72VR 6RF   Gaming    17.3  Full HD 1920x1080   \n",
       "1156     MSI     GP72M 7REX   Gaming    17.3  Full HD 1920x1080   \n",
       "\n",
       "                              Cpu   Ram                Memory  \\\n",
       "330   Intel Core i7 7700HQ 2.8GHz   8GB  256GB SSD +  1TB HDD   \n",
       "393   Intel Core i7 7700HQ 2.8GHz   8GB             256GB SSD   \n",
       "724   Intel Core i7 7700HQ 2.8GHz   8GB  128GB SSD +  1TB HDD   \n",
       "917   Intel Core i7 7700HQ 2.8GHz  16GB  256GB SSD +  1TB HDD   \n",
       "1156  Intel Core i7 7700HQ 2.8GHz  16GB  256GB SSD +  1TB HDD   \n",
       "\n",
       "                             Gpu       OpSys Weight  Price_euros  \n",
       "330   Nvidia GeForce GTX 1050 Ti  Windows 10  2.7kg      1199.00  \n",
       "393   Nvidia GeForce GTX 1050 Ti  Windows 10  2.7kg      1349.00  \n",
       "724   Nvidia GeForce GTX 1050 Ti  Windows 10  2.7kg      1348.48  \n",
       "917   Nvidia GeForce GTX 1050 Ti  Windows 10  2.7kg      1599.00  \n",
       "1156  Nvidia GeForce GTX 1050 Ti  Windows 10  2.7kg      1492.80  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clients_choice = df[\n",
    "                    (df['Company'] == 'MSI') &\n",
    "                    ~(df['Inches'] == 15.6) &\n",
    "                    (df['Gpu'] == 'Nvidia GeForce GTX 1050 Ti')\n",
    "                ]\n",
    "clients_choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1199.0, 1599.0)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clients_choice['Price_euros'].min(), clients_choice['Price_euros'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Что дешевле?\n",
    "\n",
    "В среднем дешевле ноутбуки с CPU Intel Core i7 7700HQ 2.8GHz или с Intel Core i7 7600U 2.8GHz?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1760.4084246575342"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Cpu'] == 'Intel Core i7 7700HQ 2.8GHz']['Price_euros'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1915.710769230769"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Cpu'] == 'Intel Core i7 7600U 2.8GHz']['Price_euros'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 задача\n",
    "\n",
    "Найдите самый легкий ноутбук\n",
    "\n",
    "Но обратите внимание на тип и представление данных в признаке Weight, если что, замените в строке 'kg' на пустую строку через метод .str.replace()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.69kg'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Weight'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.37\n",
       "1       1.34\n",
       "2       1.86\n",
       "3       1.83\n",
       "4       1.37\n",
       "        ... \n",
       "1298     1.8\n",
       "1299     1.3\n",
       "1300     1.5\n",
       "1301    2.19\n",
       "1302     2.2\n",
       "Name: Weight, Length: 1303, dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Weight'] = df['Weight'].str.replace('kg', '')\n",
    "df['Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.37\n",
       "1       1.34\n",
       "2       1.86\n",
       "3       1.83\n",
       "4       1.37\n",
       "        ... \n",
       "1298    1.80\n",
       "1299    1.30\n",
       "1300    1.50\n",
       "1301    2.19\n",
       "1302    2.20\n",
       "Name: Weight, Length: 1303, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Weight'] = df['Weight'].astype('float')\n",
    "df['Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Weight'].min()"
   ]
  }
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